{"id":3877,"date":"2025-05-16T07:02:36","date_gmt":"2025-05-16T07:02:36","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/05\/16\/2505-09803\/"},"modified":"2025-05-16T07:02:36","modified_gmt":"2025-05-16T07:02:36","slug":"2505-09803","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/05\/16\/2505-09803\/","title":{"rendered":"LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data"},"content":{"rendered":"

LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data
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arXiv:2505.09803v1 Announce Type: new
\nAbstract: In many scientific and industrial applications, we are given a handful of instances (a ‘small ensemble’) of a spatially distributed quantity (a ‘field’) but would like to acquire many more. For example, a large ensemble of global temperature sensitivity fields from a climate model can help farmers, insurers, and governments plan appropriately. When acquiring more data is prohibitively expensive — as is the case with climate models — statistical emulation offers an efficient alternative for simulating synthetic yet realistic fields. However, parameter inference using maximum likelihood estimation (MLE) is computationally prohibitive, especially for large, non-stationary fields. Thus, many recent works train neural networks to estimate parameters given spatial fields as input, sidestepping MLE completely. In this work we focus on a popular class of parametric, spatially autoregressive (SAR) models. We make a simple yet impactful observation; because the SAR parameters can be arranged on a regular grid, both inputs (spatial fields) and outputs (model parameters) can be viewed as images. Using this insight, we demonstrate that image-to-image (I2I) networks enable faster and more accurate parameter estimation for a class of non-stationary SAR models with unprecedented complexity.<\/div>\n

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\n Antony Sikorski, Michael Ivanitskiy, Nathan Lenssen, Douglas Nychka, Daniel McKenzie
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LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data arXiv:2505.09803v1 Announce Type: new Abstract: In many scientific and industrial applications, we are given a handful of instances (a ‘small ensemble’) of a spatially distributed quantity (a ‘field’) but would like to acquire many more. For example, a large ensemble of global temperature sensitivity fields […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,112],"tags":[2702,845,491],"class_list":["post-3877","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-stat-ml","tag-fields","tag-image","tag-networks"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/3877"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=3877"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/3877\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=3877"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=3877"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=3877"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}